7 Essential Insights for Testing Code You Didn't Write

Introduction: In the era of large language models (LLMs) and AI-driven agents, software development is undergoing a seismic shift. Traditional testing methods assume you know exactly what your code does—but what if you don't? With non-deterministic outputs, MCP servers, and auto-generated code, testers face unprecedented challenges. In a recent discussion, SmartBear's VP of AI and Architecture, Fitz Nowlan, joined Ryan to explore how old assumptions are crumbling and what new strategies—like data locality and data construction—are taking their place. Here are seven things you need to know about testing code when the source is a black box.

1. The Collapse of Old Assumptions in Software Development

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For decades, software testing relied on a foundational assumption: you can trace, predict, and control your code's behavior. But with LLM-driven agents and MCP (Model Context Protocol) servers, that assumption evaporates. The code you didn't write—generated by a model—may produce different outputs each time, even with identical inputs. This non-determinism shatters the bedrock of unit testing, regression suites, and static analysis. Developers must now accept that certain behaviors are probabilistic, not deterministic. The shift requires a mindset change: instead of asking "does the code do what I expect?" you ask "does the code do something safe and useful?" Testing becomes about establishing boundaries and acceptable ranges rather than exact matches.

2. LLM-Driven Agents: A New Class of Non-Determinism

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LLM-driven agents are not like traditional software. They generate responses, decisions, and even new code on the fly, influenced by probabilistic models. This non-determinism is inherent—it's not a bug. Testing such agents means you cannot write a simple assertion like assert output == expected. Instead, you must validate against a range of plausible answers or measure against heuristics (e.g., toxicity, relevance). For example, testing a customer support bot might involve checking that it never uses offensive language, even if the exact wording varies. The challenge multiplies when agents compose multiple subtasks, as each step adds a layer of unpredictability. New testing frameworks must embrace fuzzing, property-based testing, and behavioral contracts.

3. MCP Servers: Testing the Communication Layer

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MCP (Model Context Protocol) servers are the glue between LLMs and external tools. They orchestrate context exchange and tool invocation. But testing these servers is tricky because they sit at the intersection of deterministic APIs and non-deterministic AI. How do you verify that an MCP server correctly passes context to a vector database or a calculator tool? The answer lies in integration tests that isolate the server from the LLM—use dummy models to send known inputs and check outputs. Also, test for failure modes: what happens if the LLM returns nonsense? Does the server degrade gracefully? Data locality becomes key here: you want to test that the server uses the correct local data, not just any random generation.

4. Data Locality Trumps Source Code Control

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When source code is easy to generate (thanks to LLMs), the real value shifts to data—specifically, the data locality and data construction processes. Instead of meticulously testing each line of generated code, focus on the quality and provenance of the data that the code consumes and produces. If you know your test data is well-curated, realistic, and covers edge cases, the generated code is more likely to work correctly. This is a reversal of traditional wisdom: data testing becomes primary, code testing secondary. For instance, test that an incoming event stream has the right schema and constraints before even running the code. Data locality also means understanding where data resides—locally vs. remote—to test latency and privacy.

5. Data Construction: The New Testing Artifact

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Since you can't trust the source code to be stable, you need to invest in constructing high-quality test datasets that define expected behaviors. Data construction involves creating synthetic datasets that probe the boundaries of your system—for example, adversarial examples, rare edge cases, or multiple valid outputs for the same input. This is especially critical for LLM-based applications where outputs are non-deterministic. Use property-based testing to define invariants: for every input, the output should be in a valid range, or the system should never crash. Tools like Hypothesis (Python) or FsCheck (F#) can generate random inputs and check properties over many runs.

6. Probabilistic Assertions Replace Hard Asserts

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Traditional testing uses binary pass/fail assertions. But with non-deterministic code, you need probabilistic assertions that say, "this should pass at least 95% of the time" or "outputs should fall within a certain confidence interval." For example, when testing an LLM-based summarizer, you might assert that the generated summary has a ROUGE-L score above 0.4 on average over 100 runs. This shifts testing from verification to statistical validation. It also requires running tests many times to gather metrics—meaning test suites become slower and more resource-intensive. Engineers must trade off thoroughness for speed, using canary testing or shadow mode in production.

7. The Future: Observability-First Testing

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Given the inability to fully predict behavior, observability becomes the new testing bedrock. Instead of trying to catch every bug before deployment, you monitor for anomalies in real-time. Logs, metrics, and traces for LLM calls, MCP server interactions, and data flows allow you to detect failures after the fact—but quickly. This is akin to chaos engineering: you accept that unknown code will produce unknown outcomes, so you build robust monitoring and automated rollback. Testing then involves injecting known bad data and verifying that observability catches it. The ultimate goal is to have a continuous feedback loop: from test data construction to production monitoring to data refinement.

Conclusion: Testing code you didn't write—especially when that code is generated by LLMs or runs inside MCP servers—forces a revolution in quality assurance. Old assumptions of determinism and complete code understanding are no longer valid. Instead, embrace data locality, probabilistic assertions, and observability. By shifting focus from source code to data construction and from rigid tests to statistical validation, you can still achieve reliable software. The future of testing is not about knowing every line of code—it's about understanding the space of possible behaviors and keeping your system within safe bounds.